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Database and Expert Systems Applications
The two-volume set LNCS constitutes the proceedings of the 37th International Conference on Database and Expert Systems Applications, DEXA 2026, held in Graz, Austria, in August 11–13, 2026.The 34 full and 20 short papers presented in this set together with 2 invited papers were carefully reviewed and selected from 123 submissions. They were organized in topical sections as follows:Part I: Knowledge Graphs and Graph Intelligence; Graph Data Management and Processing; Recommendation, Search and Information Retrieval; Large Language Models and Multi-Agent Systems; Data Governance, Quality and Integration; Database Systems and Query Processing.Part II: Storage Systems and Data Infrastructure; Machine Learning Foundations and Data Generation; Temporal Data Analytics and Forecasting; AI for IoT and Cybersecurity; Neural Networks and Applied AI; Systems, Security and Emerging Applications.
R 3,137.27
Applied Data Science in Tourism
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau
R 4,247.33
R 764.00
Data Science for Fake News
This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools.The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news.The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
R 5,692.39
Fuzzy Cognitive Maps for Applied Sciences and Engineering
Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation.During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results. The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.
R 4,030.00
Spatio-Temporal Graph Data Analytics
This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms.In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.
R 3,782.78
Graph-Theoretic Concepts in Computer Science
The 26th International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2000) was held at Waldhaus Jakob, in Konstanz, Germany, on 15{ 17 June 2000. It was organized by the Algorithms and Data Structures Group of the Department of Computer and Information Science, University of K- stanz, and sponsored by Deutsche Forschungsgemeinschaft (DFG) and Univ- sit¨atsgesellschaft Konstanz. The workshop aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computer science, or by extracting new problems from applications. The goal is to present recent research results and to identify and explore directions for future research. The workshop looks back on a remarkable tradition of more than a quarter of a century. Previous Workshops have been organized in various places in Europe, and submissions come from all over the world. This year, 57 attendees from 13 di erent countries gathered in the relaxing atmosphere of Lake Constance, also known as the Bodensee. Out of 51 submis- ons, the program committee carefully selected 26 papers for presentation at the workshop. This selection re?ects current research directions, among them graph and network algorithms and their complexity, algorithms for special graph cl- ses, communication networks, and distributed algorithms. The present volume contains these papers together with the survey presented in an invited lecture by Ingo Wegener (University of Dortmund) and an extended abstract of the invited lecture given by Emo Welzl (ETH Zuric ¨ h).
R 2,015.00
Database and Expert Systems Applications
The two-volume set LNCS constitutes the proceedings of the 37th International Conference on Database and Expert Systems Applications, DEXA 2026, held in Graz, Austria, in August 11–13, 2026.The 34 full and 20 short papers presented in this set together with 2 invited papers were carefully reviewed and selected from 123 submissions. They were organized in topical sections as follows:Part I: Knowledge Graphs and Graph Intelligence; Graph Data Management and Processing; Recommendation, Search and Information Retrieval; Large Language Models and Multi-Agent Systems; Data Governance, Quality and Integration; Database Systems and Query Processing.Part II: Storage Systems and Data Infrastructure; Machine Learning Foundations and Data Generation; Temporal Data Analytics and Forecasting; AI for IoT and Cybersecurity; Neural Networks and Applied AI; Systems, Security and Emerging Applications.
R 3,144.03
Applied Categorical Data Analysis and Translational Research
An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy-to-follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real-world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book's examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.
R 4,460.00
Algebraic Foundations for Applied Topology and Data Analysis
This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user’s guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.
R 2,354.58
Applied Data Science with Python and Jupyter
Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.
R 1,140.00
Applied Geospatial Data Science with Python
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important conceptsKey FeaturesLearn how to integrate spatial data and spatial thinking into traditional data science workflowsDevelop a spatial perspective and learn to avoid common pitfalls along the wayGain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expandedBook DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learnUnderstand the fundamentals needed to work with geospatial dataTransition from tabular to geo-enabled data in your workflowsDevelop an introductory portfolio of spatial data science work using PythonGain hands-on skills with case studies relevant to different industriesDiscover best practices focusing on geospatial data to bring a positive change in your environmentExplore solving use cases, such as traveling salesperson and vehicle routing problemsWho this book is forThis book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
R 1,889.00
Concepts, Methods and Practical Applications in Applied Demography
This textbook offers a comprehensive overview of applied demography by presenting both basic concepts and methodological techniques. It allows students from the social and human sciences, demographers, consultants and anyone interested in applied demography to gain an understanding of a wide range of practical applications of demographic concepts, methods and techniques to real- world problems. Featured sidebars highlight relevant terms and concepts and case studies and exercises throughout the book offer first-hand exposure to demographic applications. Charts and graphs supplement the presentation of demographic concepts and a glossary provides an inventory of relevant terms. The first section reviews basic components of applied demography as a context for understanding and addressing societal issues. It details the methods, techniques and data sources applied by demographers in a variety of areas. Coverage includes cohort analysis, data standardization, population estimation, and the use of geographic in- formation systems (GIS). The second section focuses on the substantive areas in which demography is currently applied. The topics covered include business demography, health demography, political demography, educational demography, and applications to urban and regional planning. The book illustrates the many ways in which demographers contribute to the formulation of public policy and the resolution of societal issues.
R 3,313.62
The Applied Data Science Workshop
Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data problems.Key FeaturesGain useful insights into data science and machine learningExplore the different functionalities and features of a Jupyter NotebookDiscover how Python libraries are used with Jupyter for data analysisBook DescriptionFrom banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security.Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples.Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data.By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects.What you will learnUnderstand the key opportunities and challenges in data scienceUse Jupyter for data science tasks such as data analysis and modelingRun exploratory data analysis within a Jupyter NotebookVisualize data with pairwise scatter plots and segmented distributionAssess model performance with advanced validation techniquesParse HTML responses and analyze HTTP requestsWho this book is forIf you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.
R 1,335.00
Applying Data Science
This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.
R 3,715.94
Graph-Grammars and Their Application to Computer Science
The generic term "graph-grammars" refers to a variety of methods for specifying (possibly infinite) sets of graphs or sets of maps. The area of graph-grammars originated in the late 60s motivated by considerations concerning pattern recognition - since then the list of areas which have interacted with the development of graph-grammars has grown quite impressively. It includes pattern recognition, software specification and development, VLSI layout schemes, data bases, lambda-calculus, analysis of concurrent systems, massively parallel computer architectures, incremental compilers, computer animation, complexity theory, developmental biology, music composition, representation of physical solids, and many others. This volume is based on the contributions presented at the third international workshop on graph-grammars and their applications, held in Warrenton, Virginia, USA in December 1986. Aiming at the best possible representation of the field not all of the papers presented at the meeting appear in this volume and some of the papers from this volume were not presented at the workshop. The volume consists of two parts: Part I presents tutorial introductions to a number of basic graph and map rewriting mechanisms. Part II contains technical contributions. This collection of papers provides the reader with an up-to-date overview of current trends in graph-grammars.
R 3,090.38
Applied Sciences in Graphic Communication and Packaging
This book includes a selection of reviewed papers presented at the 49th Conference of the International Circle of Educational Institutes for Graphic Arts Technology and Management & 8th China Academic Conference on Printing and Packaging, which was held on May 14-16, 2017 in Beijing, China. The conference was jointly organized by the Beijing Institute of Graphic Communication, China Academy of Printing Technology, and International Circle of Educational Institutes for Graphic Arts Technology and Management. With eight keynote talks and 200 presented papers on graphic communication and packaging technologies, the event attracted more than 400 scientists. The proceedings cover the latest advances in color science and technology; image processing technology; digital media technology; digital process management technology in packaging; packaging, etc., and will be of interest to university researchers, R&D engineers and graduate students in the graphic arts, packaging, color science, image science, material science, computer science, digital media and network technology.
R 9,076.91
Elements of Network Science
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed.The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online.The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.
R 5,197.96
Network Models for Data Science
This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.
R 3,210.00
Transactions on Computational Science XXXVIII
The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.This, the 38th issue of the Transactions on Computational Science, is devoted to research on modelling, optimization, and graphs, with applications in 3D and sketch modelling, engineering design, evolutionary computing, and networks.
R 2,150.97
Concepts, Methods and Practical Applications in Applied Demography
This textbook offers a comprehensive overview of applied demography by presenting both basic concepts and methodological techniques. It allows students from the social and human sciences, demographers, consultants and anyone interested in applied demography to gain an understanding of a wide range of practical applications of demographic concepts, methods and techniques to real- world problems. Featured sidebars highlight relevant terms and concepts and case studies and exercises throughout the book offer first-hand exposure to demographic applications. Charts and graphs supplement the presentation of demographic concepts and a glossary provides an inventory of relevant terms. The first section reviews basic components of applied demography as a context for understanding and addressing societal issues. It details the methods, techniques and data sources applied by demographers in a variety of areas. Coverage includes cohort analysis, data standardization, population estimation, and the use of geographic in- formation systems (GIS). The second section focuses on the substantive areas in which demography is currently applied. The topics covered include business demography, health demography, political demography, educational demography, and applications to urban and regional planning. The book illustrates the many ways in which demographers contribute to the formulation of public policy and the resolution of societal issues.
R 1,832.83
Applied Data Science
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
R 6,594.00
Applied Statistics and Data Science
This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.
R 5,814.55
Foundations of Data Science
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
R 2,443.25
Applying Data Science and Learning Analytics Throughout a Learner's Lifespan
Research in the domains of learning analytics and educational data mining has prototyped an approach where methodologies from data science and machine learning are used to gain insights into the learning process by using large amounts of data. As many training and academic institutions are maturing in their data-driven decision making, useful, scalable, and interesting trends are emerging. Organizations can benefit from sharing information on those efforts.Applying Data Science and Learning Analytics Throughout a Learner's Lifespan examines novel and emerging applications of data science and sister disciplines for gaining insights from data to inform interventions into learners' journeys and interactions with academic institutions. Data is collected at various times and places throughout a learner's lifecycle, and the learners and the institution should benefit from the insights and knowledge gained from this data. Covering topics such as learning analytics dashboards, text network analysis, and employment recruitment, this book is an indispensable resource for educators, computer scientists, faculty of higher education, government officials, educational administration, students of higher education, pre-service teachers, business professionals, researchers, and academicians.
R 6,403.61
R 3,659.00
Applying Data Science
This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.
R 4,192.83
Graph-Theoretic Concepts in Computer Science
This book constitutes the thoroughly refereed proceedings of the 38th International Workshop on Graph Theoretic Concepts in Computer Science (WG 2012) held in Jerusalem, Israel on June 26-28, 2012. The 29 revised full papers presented were carefully selected and reviewed from 78 submissions. The papers are solicited describing original results on all aspects of graph-theoretic concepts in Computer Science, e.g. structural graph theory, sequential, parallel, randomized, parameterized, and distributed graph and network algorithms and their complexity, graph grammars and graph rewriting systems, graph-based modeling, graph-drawing and layout, random graphs, diagram methods, and support of these concepts by suitable implementations. The scope of WG includes all applications of graph-theoretic concepts in Computer Science, including data structures, data bases, programming languages, computational geometry, tools for software construction, communications, computing on the web, models of the web and scale-free networks, mobile computing, concurrency, computer architectures, VLSI, artificial intelligence, graphics, CAD, operations research, and pattern recognition
R 2,183.44
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
R 10,210.61
Practitioner's Guide to Graph Data
Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique intersection known as graph thinking.Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You'll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application.Build an example application architecture with relational and graph technologiesUse graph technology to build a Customer 360 application, the most popular graph data pattern todayDive into hierarchical data and troubleshoot a new paradigm that comes from working with graph dataFind paths in graph data and learn why your trust in different paths motivates and informs your preferencesUse collaborative filtering to design a Netflix-inspired recommendation system
R 1,824.00
Database Safari
Pack your field kit—this is a guided safari through today’s database ecosystem. In the wild, one size rarely fits all: Modern systems often need specialized databases to deliver real‑world performance and scale. This book is a practical field guide to those less commonly used databases—vector, time‑series, graph, columnar/OLAP, geospatial, event‑stream, and more. It shows you how each engine works, where it excels, and how to choose the right engine for each workload. The book provides clear trade‑off tables, indexing strategies, query patterns, and use‑case recipes you can apply immediately.Starting with foundations in SQL and NoSQL, this book explores each “species” chapter by chapter, emphasizing hands‑on scenarios and pitfalls to avoid. It then brings everything together in polyglot/hybrid architectures, helping you combine engines confidently, whether you’re designing a similarity search with vector databases (ANN), building observability pipelines on time‑series data and event logs, modeling relationships in graph databases, or delivering analytics with columnar/OLAP and geospatial queries. The result is a concise, engineer‑first map of the database landscape that turns theory into pragmatic decision‑making.What You Will LearnIdentify the right engine for the job across vector, time‑series, graph, columnar/OLAP, geospatial, and event‑stream databasesEvaluate trade‑offs in performance, scalability, consistency, storage layout, indexing, and operational complexityDesign polyglot architectures that combine multiple engines (e.g., vector + OLAP + event‑stream) for end‑to‑end solutionsApply use‑case patterns for similarity search (ANN), observability metrics, fraud/network analysis, geospatial routing, and analytical reportingUnderstand core internals that matter (index types, retention policies, log‑structured storage, spatial indexes, columnar execution) without vendor lock‑inAvoid common pitfalls in schema design, query planning, and cross‑engine data movementBuild a decision checklist and trade‑off tables to justify engine selection to stakeholdersWho This Book Is ForSoftware engineers; back-end developers; data engineers; system architects; site reliability engineers (SREs); technical leads with basic SQL/NoSQL experience who need practical guidance to select, compare, and combine specialized databases for real‑world applications
R 980.00
Complete Guide to Graph Representation Learning with Case Studies
Comprehensive resource on graph representation learning (GRL), exploring fundamental principles, advanced methodologies, and case studies A Complete Guide to Graph Representation Learning with Case Studies provides a concise understanding of the subject of graph representation learning (GRL), a rapidly advancing field in the domain of machine learning. The book explores basic concepts to state-of-the-art techniques, enabling readers to progress from a fundamental understanding of the approach to mastering its application. The authors also cover the topics of graph embedding methods, graph neural network (GNN) -based approaches, and the latest trends in GRL such as deep learning, transfer learning, graph pooling, alignment, and matching, and graph machine learning. The book includes examples of applications of graph learning methods with real-world case studies in which the covered methods can be utilized. It also includes innovative solutions to graph machine learning problems such as node classification, link prediction, and unsupervised learning, and discusses neighborhood overlap visualization techniques and overlapping neighborhoods in heterogeneous graphs. Finally, the book provides an overview of open and ongoing research directions and student projects, providing a glimpse into potential avenues for future work. The book also includes information on: Node-level features such as node degree, node centrality, closeness, betweenness, eigenvector, page rank centrality, clustering coefficient, closed triangles, egograph, and motifsNeighborhood sampling techniques such as breadth-first sampling, depth-first sampling, snowball sampling, random walk, shallow walk, edge sampling, link-based sampling, and metapath-based samplingDeep learning models including Graph Autoencoder (GAE), Variational Graph Encoder (VGAE), and Graph Attention Network (GAN)Graph alignment and matching, covering subgraph matching and embedding for matching A Complete Guide to Graph Representation Learning with Case Studies is a thorough and up-to-date reference on the subject for engineers and researchers in data science and machine learning as well as graduate students in related programs of study.
R 4,579.00
Graph Learning Techniques
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
R 5,626.28
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,587.11
Applied Statistics and Data Science
This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.
R 6,015.37
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It presents fundamental and advanced concepts in intelligent computing paradigms, data science, graph theory, and mathematical modeling. As high-performance computing evolves, the emphasis on intelligent, adaptive computing mechanisms and the integration of mathematical modeling into computational algorithms is becoming increasingly vital. Serving as a valuable resource for professionals and newcomers alike, this book provides insights into enhanced computing paradigms and mathematical approaches, ranging from foundational to advanced levels. Our objective is to create a platform where researchers, engineers, academicians, and industry experts worldwide can exchange findings on emerging trends. Beyond introducing innovative concepts, the authors believe this book will spark meaningful discussions and inspire new ideas.
R 7,326.00
Network Science with Python
Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in colorKey FeaturesCreate networks using data points and informationLearn to visualize and analyze networks to better understand communitiesExplore the use of network data in both - supervised and unsupervised machine learning projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNetwork analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level.By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.What you will learnExplore NLP, network science, and social network analysisApply the tech stack used for NLP, network science, and analysisExtract insights from NLP and network dataGenerate personalized NLP and network projectsAuthenticate and scrape tweets, connections, the web, and data streamsDiscover the use of network data in machine learning projectsWho this book is forNetwork Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.
R 1,693.00
Advanced Multimodal Compatibility Modeling and Recommendation
This Third Edition sheds light on state-of-the-art theories and practices in multimodal compatibility modeling and recommendation, offering comprehensive insights into this evolving field. This topic, and fashion compatibility modeling in particular, has garnered increasing research attention in recent years due to the significant economic impact of e-commerce. Building upon recent research and the prior edition, the authors present a series of graph-learning based multimodal compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This book introduces a number of advanced multimodal compatibility modeling and recommendation methods, including category-guided multimodal compatibility modeling and try-on-guided multimodal compatibility modeling. The authors also provide comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.
R 1,649.00
R 519.00
Geospatial Data Science Techniques and Applications
Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.
R 2,954.90
Applied Data-Centric Social Sciences
Applied data-centric social sciences aim to develop both methodology and practical applications of various fields of social sciences and businesses with rich data. Specifically, in the social sciences, a vast amount of data on human activities may be useful for understanding collective human nature. In this book, the author introduces several mathematical techniques for handling a huge volume of data and analysing collective human behaviour. The book is constructed from data-oriented investigation, with mathematical methods and expressions used for dealing with data for several specific problems. The fundamental philosophy underlying the book is that both mathematical and physical concepts are determined by the purposes of data analysis. This philosophy is shown throughout exemplar studies of several fields in socio-economic systems. From a data-centric point of view, the author proposes a concept that may change people’s minds and cause them to start thinking from the basis of data. Several goals underlie the chapters of the book. The first is to describe mathematical and statistical methods for data analysis, and toward that end the author delineates methods with actual data in each chapter. The second is to find a cyber-physical link between data and data-generating mechanisms, as data are always provided by some kind of data-generating process in the real world. The third goal is to provide an impetus for the concepts and methodology set forth in this book to be applied to socio-economic systems.
R 2,015.00
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
R 3,152.68
R 512.00
R 611.00
